Healthcare Mistreatment, State-Level Policy Protections, and Healthcare Avoidance Among Gender Minority People

Introduction: This study examined whether past experiences of mistreatment in healthcare were associated with greater healthcare avoidance due to anticipated mistreatment among gender minority (GM) people. We evaluated whether state-level healthcare policy protections moderated this relationship. Methods: Data from the 2018 Annual Questionnaire of The PRIDE Study, a national longitudinal study on sexual and gender minority people’s health, were used in these analyses. Logistic regression modeling tested relationships between lifetime healthcare mistreatment due to gender identity or expression and past-year healthcare avoidance due to anticipated mistreatment among GM participants. Interactions between lifetime healthcare mistreatment and state-level healthcare policy protections and their relationship with past-year healthcare avoidance were tested. Results: Participants reporting any lifetime healthcare mistreatment had greater odds of past-year healthcare avoidance due to anticipated mistreatment among gender expansive people (n = 1290, OR = 4.71 [CI]: 3.57–6.20), transfeminine people (n = 263, OR = 10.32 [CI]: 4.72–22.59), and transmasculine people (n = 471, OR = 3.90 [CI]: 2.50–6.13). Presence of state-level healthcare policy protections did not moderate this relationship in any study groups. Conclusions: For GM people, reporting lifetime healthcare mistreatment was associated with healthcare avoidance due to anticipated mistreatment. State-level healthcare policy protections were not a moderating factor in this relationship. Efforts to evaluate the implementation and enforcement of state-level policies are needed. Continued efforts to understand instances of and to diminish healthcare mistreatment of GM people are recommended

Patient Flow and Capacity Management in Health Services

Growing demand for health services provided by outpatient clinics and hospitals caused health institutions flow and capacity challenges. Health organizations’ poor response to these challenges directly translate into negative patient outcomes and intensified downstream costs. In this study, we investigate dynamics and mechanisms that influence patient wait times and capacity strains and propose strategies and policies that can improve these issues in both ambulatory and inpatient care. First, we investigate the access issue in a multidisciplinary memory clinic, which consists of three practices and six patient types. Considering patient flow and interactions, we develop an empirical simulation model to evaluate the effectiveness of access improvement strategies such as overbooking, repatriation (i.e., referring the patient back to primary care), and increasing provider hours. Our results suggest that despite the increasing wait times in the multidisciplinary memory clinic, increasing provider slots is not always an effective strategy. In fact, overbooking and reducing unnecessary follow-up visits can result in more significant performance improvements. Second, we study the impact of long-stay patients (i.e., patients with discharge barriers that stay in the hospital for non-medical reasons) on flow and capacity. In particular, we focus on the patient flow between Intensive Care Unit (ICU), Step-down Unit (SDU), and Medical Unit (MU) and quantify the impact of long-stay patient volumes on wait time, length of stay (LOS), and 30- day readmission probability of other patients transitioning among these units. We find that larger proportion of long-stay patients in the MU results in shorter LOS for other patients in the MU, and longer wait time for patients leaving the ICU to MU. Third, we examine existing patient grouping system based on the service lines at two hospitals within the same health system and propose a two-step clustering-classification approach to identify new patient clusters. Unlike existing 8 patient clusters (i.e., service lines), our results identified 11 patient clusters in Wilmington hospital and 15 patient clusters in Christiana hospital, indicating the need to further splitting some of the existing service lines such as internal medicine, general surgery, and neurological disorders

An interpretable clustering classification approach for assessing and adjusting hospital service lines

Hospital beds are often assigned among several major groups called service lines, each aimed to provide care for patients with similar medical needs such as cancer, musculoskeletal disorders, vascular, surgical, medical, and women and children. Besides the benefits of service lines in streamlining operations, they can lead to unintended issues if they are not assessed and adjusted regularly. These issues include fragmentation and inconsistencies in care, offering overlapping services, uneven resources, and growth, disparities in access to care, imbalanced capacity utilization, and system-wide flow issues. We propose an interpretable clustering classification approach for assessing and adjusting existing service lines regarding size and patient distribution. Our approach is useable in practice as it uses data available during patient admission and generates interpretable rules for patient assignment among service lines. Our results from two academic hospitals suggest the need for further splitting service lines such as internal medicine, general surgery, and neurological disorders. Further, our findings support the idea of providing specialized services such as orthopedic surgery, cardiology, physical medicine, and pregnancy, childbirth and the puerperium. These findings have several practical implications related to patient mix, capacity planning, bed assignment, and hospital administration overall